the evaluated systems were not designed to run several tasks in parallel; for instance, we managed to run MAMBA outside SEALS, but it relies on a MySQL database and raised a concurrent access exception.

Impact on the f-measure.
As shown in Section REF , the impact of the number of divisions on the f-measure depends on the evaluated systems. In the near future we aim at conducting an extensive evaluation of our framework over OAEI systems able to deal with the largest tasks in order to obtain more insights about the impact on the f-measure. In [1]} we reported a preliminary evaluation where YAM-Bio [2]} and AML [3]} kept similar f-measure values, while LogMap [4]} had a reduction in f-measure, as the number of divisions increased.
Number of subdivisions.
Currently our strategy requires the size of the
number of matching subtasks or divisions as input. The (required) matching subtasks may be known before
hand if, for example, the matching tasks are to be run in parallel in a number
of available CPUs. For the cases where the resources are limited or where a
matching system is known to cope with small ontologies, we plan to design an
algorithm to estimate the number of divisions so that
the size of the matching subtasks in the computed divisions is appropriate to the system and resource constraints.
Dealing with a limited or large lexicon.
The construction of LexI shares a limitation with state-of-the-art systems when the input ontologies are lexically disparate or in different languages.
In such cases, LexI can be enriched with general-purpose lexicons (e.g., WordNet), more specialised background knowledge (e.g., UMLS Metathesaurus) or with translated labels using online services (e.g., Google).
On the other hand, a large lexicon may also have an important impact in the computation times. Our conducted evaluation shows, however, that we can cope with very large ontologies with a rich lexicon (e.g., NCI Thesaurus).
Notion of context.
Locality-based modules are typically much smaller than the whole ontology and they have led to very good results in terms of size and coverage. We plan, however,
to study different notions of context
of an alignment (e.g., the tailored modules proposed in
[5]}) to further improve the results in terms of size while keeping the same level of coverage.
This work was supported by the SIRIUS Centre for Scalable Data Access (Norges forskningsråd), the AIDA project (Alan Turing Institute), Samsung Research UK, Siemens AG, and the EPSRC projects AnaLOG, OASIS and UK FIRES. We would also like to thank the anonymous reviewers that helped us improve this work.
